Evolutionary Models of Bargaining: Comparing Agent-based Computational and Analytical Approaches to Understanding Convention Evolution

This paper compares two methodologies that have been used to understand the evolution of bargaining conventions. The first is the analytical approach that employs a standard learning dynamic and computes equilibria numerically. The second approach simulates an environment with a finite population of interacting agents. We compare these two approaches within the context of three variations on a common model. In one variation agents randomly experiment with different demands. A second variation posits assortative interactions, and the third allows for sophistication in agent strategies. The simulation results suggest that the agent-based approach performs well in selecting equilibria in most instances, but exact predicted population distributions often vary from those calculated numerically. Classification Numbers: C63, C73, C78.